Lagged variables play an important role in cross-sectional models in geography and regional sciences. This paper starts with an overview of the situations in which they may be required. Lagged variables also pose serious problems from a statistical point of view: multicollinearity and the determination of the length of the lag. Some common approaches to these two problems are discussed and evaluated. As an alternative a linear structural equation approach is presented, where the lagged variables are compressed to latent variables in a measurement model. The relationship between the lagged variables, thus compressed, and the dependent variable is expressed in the structural model. Both the measurement model and the structural model are estimated simultaneously. The paper ends with an application. A model of urban immigration for the thirty-three largest Dutch cities is estimated.
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